The importance of small artificial water bodies as sources of methane emissions in Queensland, Australia

The importance of small artificial water bodies as sources of methane emissions

The importance of small artificial water bodies as sources of methane emissions in Queensland, AustraliaThe importance of small artificial water bodies as sources of methane emissionsAlistair Grinham et al.

Emissions from flooded land represent a direct source of anthropogenic
greenhouse gas (GHG) emissions. Methane emissions from large, artificial water
bodies have previously been considered, with numerous studies assessing
emission rates and relatively simple procedures available to determine their
surface area and generate upscaled emissions estimates. In contrast, the role
of small artificial water bodies (ponds) is very poorly quantified, and
estimation of emissions is constrained both by a lack of data on their
spatial extent and a scarcity of direct flux measurements. In this study, we
quantified the total surface area of water bodies
< 105 m2 across Queensland, Australia, and emission rates
from a variety of water body types and size classes. We found that the
omission of small ponds from current official land use data has led to an
underestimate of total flooded land area by 24 %, of small artificial
water body surface area by 57 % and of the total number of artificial
water bodies by 1 order of magnitude. All studied ponds were significant
hotspots of methane production, dominated by ebullition (bubble) emissions.
Two scaling approaches were developed with one based on pond primary use
(stock watering, irrigation and urban lakes) and the other using size class.
Both approaches indicated that ponds in Queensland alone emit over 1.6 Mt
CO2 eq. yr−1, equivalent to 10 % of the state's entire
land use, land use change and forestry sector emissions. With limited data
from other regions suggesting similarly large numbers of ponds, high
emissions per unit area and under-reporting of spatial extent, we conclude
that small artificial water bodies may be a globally important missing
source of anthropogenic greenhouse gas emissions.

Over the last 20 years, greenhouse gas (GHG) emissions studies from large,
artificial water bodies such as water supplies or hydroelectric reservoirs
have clearly demonstrated these are major emissions sources. Whilst carbon
dioxide (CO2), nitrous oxide (N2O) and methane
(CH4) can all be emitted, the most recent global synthesis of
artificial water body emissions demonstrated that, when converted to
CO2 equivalents, CH4 accounted for 80 % of fluxes
(Deemer et al., 2016). Increasingly sophisticated reviews have explored the
magnitude of the artificial water body contribution to regional and global
CH4 budgets (St. Louis et al., 2000; Bastviken et al., 2011; Deemer
et al., 2016). Much of the focus in reducing the uncertainty from this
anthropogenic greenhouse gas source has focussed on the spatial and temporal
variability in total emission rates and, in particular, the relative
contribution of CH4 bubbling (ebullition) directly from the
sediment (Bastviken et al., 2011). To enable large-scale emissions estimates
from larger, artificial water bodies, relationships between eutrophication
status and sediment temperature (Aben et al., 2017; Harrison et al., 2017)
have been developed to predict both diffusive and ebullitive emission rates.
However, in regional or global scaling of emissions it is important to
examine the emission rates of all types and sizes of artificial water bodies
(Panneer Selvam et al., 2014). Furthermore the surface area of small water
bodies can be particularly difficult to quantify in national and global
datasets due to their small size and large number (Chumchal et al., 2016). In
addition, the peripheral areas of small water bodies regularly experience
periods of inundation and no inundation as water levels change due to their
relatively shallow nature and high water use rates. The changes in their
inundation status may influence emission rates as has been observed for
natural ponds (Boon et al., 1997). Given that there are estimated to be 16 million
artificial water bodies with a surface area less than 0.1 km2 (Lehner
et al., 2011), understanding the rates and variability in emissions from
these flooded lands will be an important refinement to global CH4
budgets.

The increasing urbanisation of society as well as the expansion of
agriculture and commercial mining activities has resulted in a proliferation
of small artificial water bodies in many parts of the globe (Renwick et al.,
2005; Downing et al., 2006; Pekel et al., 2016). This is well illustrated by
the example from the United States where artificial small water bodies
increased from an estimated 20 000 in 1934 (Swingle, 1970) to over 9 million
in 2005 (Renwick et al., 2005). These water bodies provide valuable services
and are required to irrigate crops, provide water for farm stock, manage
storm water, offer visual amenity and recreational activities, and supply
water for industrial processes (Fairchild et al., 2013). Small water bodies
are often avian biodiversity hotspots, for example hosting an estimated 12
million water birds in a single catchment area in the Murray–Darling river
system, Australia (Hamilton et al., 2017).

The creation of small artificial water bodies also represents a
transformation of the landscape, referred to in the Intergovernmental Panel
on Climate Change land use emission accounting procedures as “Flooded Lands”
(IPCC, 2006). Where the creation of small water bodies leads to new
greenhouse gas emissions, these emissions are considered anthropogenic
in origin according to IPCC guidelines (IPCC, 2006) and should therefore be
included in Flooded Lands emissions inventories (Panneer Selvam et al.,
2014). In addition, quantifying methane emission from ponds will improve our
understanding of their role in the global carbon cycle. The potential of
ponds as major organic carbon sinks has been established (Downing, 2010),
although the stability and permanence of organic carbon trapped within ponds
is critical to determining the magnitude of this sink. Loss pathways include
active de-siltation (Verstraeten and Poesen, 2000), breaching of fully silted
dams (Boardman and Foster, 2011) and methane emissions.

To date, the relatively few regional studies on small, artificial water
bodies (hereafter “ponds”) have focussed on water and sediment dynamics
rather than GHG emissions (Downing et al., 2008; Callow and Smettem, 2009;
Verstraeten and Prosser, 2008; Habets et al., 2014). Studies of GHG emissions
from ponds have been limited (Downing, 2010; Deemer et al., 2016) but are in
agreement with assessments of larger water bodies where CH4 is the
dominant GHG relative to N2O and CO2 (Merbach et al.,
1996; Natchimuthu et al., 2014). The only regional-scale study to date was
undertaken in India by Panneer Selvam et al. (2014). In order to quantify the
role of artificial ponds in the global CH4 cycle, as well as their
role as a source of anthropogenic emissions, it is necessary to obtain both
estimates of CH4 fluxes from a broader range of sites and also to
estimate the surface area contributing to emissions. An important part of the
value of building a dataset of CH4 flux estimates from a broad
range of sites is determining factors that account for spatial and temporal
variability in the flux. Surface area estimates can be problematic given the
range of water types (small urban lakes to large irrigation ponds) that fall
within the definition of “ponds”, their frequently high temporal variation
in surface area, the sheer number of such water bodies and their ongoing
increase in number over time.

Here, we present the first regional-scale assessment of CH4
emissions from ponds in the Southern Hemisphere and, following the assessment
of Panneer Selvam et al. (2014), only the second regional assessment globally.
The assessment was undertaken in the 1.85 million km2 state of
Queensland, Australia. Queensland provides an effective test case for the
estimation of CH4 emissions from ponds because (i) it incorporates
a high degree of spatial variability in land use and climate, from desert to
humid tropics; and (ii) the irregular rainfall patterns and wide spatial
coverage of aerial imagery result in a large number of artificial ponds,
which are relatively easy to quantify. CH4 emissions from these
ponds can be considered anthropogenic in origin, because past studies of
rainforest and agricultural soils in the region have clearly shown these
terrestrial landscapes were weak CH4 sinks (ranging from −0.02 to
−5 mg CH4 m−2 d−1) prior to inundation (Allen et
al., 2009; Scheer et al., 2011; Rowlings et al., 2012).

The principal objective of this study was to establish the GHG status of
ponds in Queensland, Australia. Given the paucity of GHG data from ponds,
this study has focussed on empirical assessments of CH4 emissions
from a range of pond types rather than detailed assessments of drivers of
these emissions. Our assessment is comprised of four components:

Quantify the area of ponds, relative to regional assessments of larger
artificial water bodies.

Quantify CH4 emission rates for a wide spectrum of pond
types.

Determine variability in their surface area and emission rates.

Determine the influence of inundation level on emission rates.

When integrated together, these components provide a robust regional
assessment of anthropogenic CH4 emissions for ponds in Queensland,
Australia.

2.1 Study area description

Queensland, the second largest state in Australia, covers a surface area of
1.85 million km2 and has a population of 4.75 million people. Land use
across the state is dominated by agriculture with over 80 % of the total
surface area utilised for grazing cattle or irrigated cropping (Fig. 2a;
QLUMP, 2018). The Queensland agriculture sector contributes more than
AUD 13 billion per year to the state economy and includes 15 million cattle
and sheep as well as 4526 km2 of land under irrigation (ABS, 2018). The
climate is subtropical or tropical with mean annual temperatures ranging from
27.5 ∘C in the state's north to 15.8 ∘C in the southern
interior. There are large gradients in rainfall across the state ranging from
a mean annual rainfall of over 3000 mm in the coastal north-east to less
than 100 mm in the arid western regions (Fig. 2b). Rainfall has a distinct
annual pattern with up to 80 % falling during the summer months from
November to April and is subject to decadal drought and flood cycles
(Klingaman et al., 2013). The economic importance of agriculture coupled with
the need to provide a year-round water supply for these activities and the
lack of predictable rainfall has resulted in the proliferation of artificial
water bodies across the state (Fig. A1 in the Appendix). However, the number
and surface area of ponds in Queensland is relatively unknown as there is no
legal requirement to refer ponds to the state registry due to their small
size. Under current state law only dam walls in excess of 10 m and volumes
above 750 ML (megalitres) are referable (DEWS, 2017) and the maximum
reported volume for ponds in Queensland is 3 times less than the referable
volume (< 250 ML) (SKM, 2012). This study has assumed ponds are
less than 100 000 m2 as this is recognised globally as the major area
of uncertainty in surface area assessments (Lehner and Döll, 2004;
Downing, 2010) and has been identified as a threshold in global lake
inventories (Downing et al., 2006; Verpoorter et al., 2014).

2.2 Relative surface area of ponds across the region

To determine the number and relative surface area of ponds across Queensland,
three state government GIS databases of artificial water bodies were
utilised. However, these databases required additional processing to extract
comparable pond data as there were inconsistencies in the format and
nomenclature of feature types. The primary database used was the most recent
official assessment of land use from March 2018 (QLUMP, 2018), and within the
primary land use classification of “Water” there is a secondary category of
artificial “Reservoirs/dam” divided further into “Reservoirs, Water
storage and Evaporation basin”. The individual water body surface area is
provided and all ponds (< 105 m2) were extracted from the
database. Evaporation basins were excluded, as these are commonly used for
salt extraction. These ponds were then compared against two state government
databases from a high-resolution assessment of artificial water bodies across
the state published in 2014 and 2015. Both databases are derived from aerial
(10 to 60 cm orthophotography) and satellite (0.5 to 2.5 m resolution)
imagery captured between 2010 and 2014. One database contains water bodies
greater than 625 m2 at full supply (Reservoirs – Queensland;
http://qldspatial.information.qld.gov.au/catalogue/, last access:
28 November 2017) and for water bodies less than 625 m2 a second
database was used (Water Storage Points – Queensland;
http://qldspatial.information.qld.gov.au/catalogue/, last access:
28 November 2017).

Water bodies larger than 625 m2 contained individual polygons where
water body surface area was provided and all water bodies less than
105 m2 were extracted from the database. The database of water
bodies smaller than 625 m2 contained point data providing only the
location of water bodies and no information on their dimensions (Fig. A1b,
c). To estimate the surface area of these systems, 100 water bodies were
randomly selected using the Subset Features tool in the Geostatistical
Analyst toolbox in ArcGIS (Version 10.3, ESRI Inc., Redlands, California,
USA). The surface area of selected water bodies was then quantified using
high-resolution aerial imagery (Nearmap; https://www.nearmap.com.au/,
last access: 15 May 2018). Typical pixel resolution of 7 cm greatly improves
edge detection of ponds as it can be very challenging to separate the water
edge from riparian vegetation stands with coarser-scale data. Pond edges were
mapped following the methodology of Albert et al. (2016) where imagery was
georeferenced and the water edge was manually traced to create individual
polygons for each pond. The mean surface area of all 100 polygons was then
assumed to approximate the surface area of all individual ponds within this
database and the total surface area was calculated by multiplying this mean
surface area by the total number of ponds.

To ensure only one water body was reported from each location, all databases
were first screened to remove repeated detections of water bodies. All
remaining water bodies were then summed together to calculate the total surface
area of ponds and this was compared to larger reservoirs to determine their
relative surface area. To undertake regional scaling of pond emissions,
individual ponds were sorted using two different size class classifications.
Firstly, we categorised sites into the three smallest size classes (102
to 103; 103 to 104; and 104 to 105 m2) in the
Global Reservoir and Dam (GRanD) assessment (Lehner et al., 2011). Secondly,
we divided sites into water bodies less than 3500 m2 (primarily stock
dams) and larger water bodies (primarily irrigation dams and urban lakes),
following the findings of Lowe et al. (2005) and SKM (2012).

2.3 CH4 emissions from broad spectrum of pond types

To quantify the range of emission rates from ponds, a monitoring program was
undertaken from August to December 2017 across a wide spectrum of ponds
including: farm dams (irrigation and stock watering), urban lakes, small weir
systems (i.e. small dams leading to widening and slowing of river flows) and
rural residential water supplies (Fig. 1). Stock dams, irrigation dams and
urban lakes account for the vast majority of ponds across Queensland and
ponds within each category were selected to represent the regional size class
distribution (Fig. A2). The majority of sites were located in coastal
catchments in south-east Queensland, Australia, as well as one urban lake and
three stock dams in central Queensland (Fig. 2c).

Figure 2(a) The 2018 statewide assessment showing the relative
surface area occupied by secondary land use categories (QLUMP, 2018). Note
the legend shows the two largest land uses within each category.
(b) Mean annual rainfall isohyets across Queensland from the
30-year period of 1961 to 1990
(http://www.bom.gov.au, last access: 13 March 2018).
(c) Location of study ponds and ponds identified from the land use
assessment (QLUMP, 2018) and two additional statewide databases (see
text).

There are a number of commonly used methods to assess methane emissions from
water bodies depending on the pathway of interest. For the diffusive emission
pathway, rates may be modelled using the thin boundary methods or directly
measured using manual or automatic floating chambers (St. Louis et al.,
2000). For ebullition pathways, rates can be directly measured using acoustic
surveys or funnel traps (DelSontro et al., 2011). Thin boundary layer models
cannot be used to quantify the ebullition pathway and acoustic surveys or
funnel traps cannot be used effectively in ponds as the water depth is often
too shallow (< 1 m). We chose to use floating chambers to capture
both ebullition and diffusive fluxes. CH4 emission rates were
measured by deploying between 3 and 16 floating chambers per water body,
covering both peripheral and central zones (Fig. A3). Chamber design followed
the recommendations of Bastviken et al. (2015), as these lightweight chambers
(diameter 40 cm, 12 L headspace volume and 0.7 kg total weight) were
ideally suited to deployment in ponds where both site access and on-water
deployments can be challenging (Fig. A4). The floating chambers used were
designed to yield negligible bias on the gas exchange and compare well with
non-invasive approaches (Cole et al., 2010; Gålfalk et al., 2013; Lorke
et al., 2015).

Where possible, 24 h measurements were undertaken; however, in three water
bodies this was not possible (Appendix Table A1) and here measurements lasted
between 6 and 8 h. The 24 h deployment time was chosen to increase the
likelihood of capturing ebullition, which is episodic in nature, and of
incorporating diel variability in diffusive emissions which can be up to a
2-fold bias (Bastviken et al., 2004, 2010; Natchimuthu et al., 2014). The use
of long-term deployments may underestimate diffusive fluxes, which decrease
as the chamber headspace approaches equilibrium with the water. However, in
contrast to CO2, CH4 has a long equilibration time and it
has been shown that a 24 h deployment of these types of flux chambers on
lakes underestimate diffusive fluxes by less than 10 % (Bastviken et al.,
2010). An initial gas sample was collected at chamber deployment and a final
chamber headspace gas sample after 24 h following the Exetainer method
described in Sturm et al. (2015). CH4 emission rates were
calculated from the change in headspace concentration over time and
normalised to areal units (Grinham et al., 2011).

2.4 Variability in surface area and emission rate

2.4.1 Spatial and seasonal variability across a single water body

To gain insight into the spatial and temporal uncertainty in pond emissions
we compared variability in seasonal emissions from a single site to emissions
from an intensive spatial survey of multiple sites across the pond (Fig. 4).
Seasonal variability in emission rates was measured at an urban lake (St
Lucia 1) where monthly monitoring at a single site was undertaken across an
annual cycle (January to December 2017). This pond was selected as water
level remains relatively constant throughout the year and sampling would not
be impacted by changes in inundation status. Emissions were monitored following
the same methodology as described in the preceding section, and four or five
floating chambers were deployed for each sampling event. Emission rates from
this seasonal study were then compared to an intensive spatial survey of the
same pond (December 2017), where 16 chambers were deployed simultaneously for
a 24 h incubation. To better understand spatial patterns in emissions within
this pond the water depth and proximity to inflow points were mapped. The
bathymetric survey was conducted using a logging GPS depth sounder (Lowrance
HDS7 depth sounder, Navico, Tulsa, Oklahoma, USA). Georeferenced water depth
points were imported into ArcGIS and interpolated across the whole water body
using the inverse distance weighting function.

2.4.2 Variability in water surface area across all monitored ponds

The variability in surface area of each of the 22 ponds monitored in the
emissions surveys was analysed using high-resolution historical imagery
across all monitored water bodies. A time series of high-resolution aerial
imagery over a 9-year period from 2009 to 2017 was screened for image quality
and appropriate images were selected. The time series data are not consistent
across the whole state; the number of discrete images for individual water
bodies varied from 3 to 16. Images of individual ponds were georeferenced to
a common permanent feature across all images and then the outer water edge
was mapped and surface area calculated following Albert et al. (2016). The
time series of surface area for individual water bodies was compared to their
corresponding surface area at full supply level (AFSL) and
expressed as a percentage then grouped into three size classes based on the
GRanD classification. This time period also captured the range of rainfall
variability across the state with 2010 being the wettest year on record
whilst 2013 to 2015 were consecutive drought years (Average rainfall;
https://data.qld.gov.au/, last access: 10 May 2018).

2.5 Effect of inundation status on pond emissions

The effect of inundation status on emission rates was tested on a stock dam
(Gatton 4) where measurements were undertaken on peripheral areas during
periods of inundation and no inundation. This pond was selected as stock dams
generally experience accelerated rates of water level change due to their
relatively small size compared to other pond types (Fig. A2). In addition,
the construction of this pond is typical for stock dams (a shallow pit is dug
out and the soil used to construct the wall and spillway) and the surface
area (1893 m2) closely matched the median for all farm dams
(1586 m2; Fig. A2). Emission measurements for the inundated period
followed the methodology outlined above for the water body emissions survey.
Three weeks later water levels within the ponds had dropped and emission
measurements were repeated at the same sites which were now exposed. For
these emission measurements five chambers (90 mm diameter, 150 mm length)
were carefully inserted 50 mm into the ground and care was taken to minimise
disturbance to the soil surface. The headspace of each chamber was flushed
with ambient air to remove headspace contamination due to chamber insertion,
then the sampling port of each chamber was sealed. After the deployment
period, a gas headspace sample was collected and CH4 concentration
was analysed.

2.6 Statistical analyses and regional scaling of emissions

Emissions rates and surface area data were analysed using a series of one-way
analyses of variance (ANOVAs) with the software program Statistica 13 (Dell
Inc., 2016). Analysis of emissions rates collected during the monthly
monitoring study and the inundation study used sampling month or inundation
status as the categorical predictors and chamber emission rates as the
continuous variable. Emission rates from individual water bodies collected
during the broad survey were first pooled into four primary use categories
(irrigation, stock, urban and weirs) or three different GRanD size classes
and these categories were used as the categorical predictors. The primary use
of each pond was provided by pond owners or managers; in the case of urban
lakes that had both aesthetic and storm water functions these were classified
as urban (Table A1). A total of 22 ponds were included in this survey with 4
irrigation ponds, 9 stock watering ponds, 7 urban ponds and 2 weirs.
Changes in water surface area (as a percentage of AFSL) from
individual water bodies were pooled into three GRanD size classes and these
categories used as the categorical predictors. Where necessary, continuous
variable data were log transformed to ensure normality of distribution and
homogeneity of variance (Levene's test) with post hoc tests performed using
Fisher's LSD (least significant difference) test (Zar, 1984). Tests for
normality were conducted using the Shapiro–Wilks test as recommended by Ruxton
et al. (2015). The non-parametric Kruskal–Wallis (KW) test was used for
continuous data which failed to satisfy the assumptions of normality and
homogeneity of variance even after transformation. Statistical results were
reported as follows: test applied (Fisher's LSD or Kruskal–Wallis test), the
test statistic (F or H) value and associated degrees of freedom with
p value.

Emissions were scaled to water body size classes following two different
approaches. Firstly, emissions were grouped according to their respective
GRanD size class. These match the size class of water bodies used in the
emissions monitoring of this study, and the GRanD database was used in the
most recent global synthesis of greenhouse gas emissions from reservoirs
(Deemer et al., 2016). Secondly, water bodies less than 3500 m2 in area
were assumed to be primarily stock dams and larger water bodies primarily
irrigation dams (Lowe et al., 2005). To extrapolate pond emission rates to
regional scales, an appropriate measure of centrality should be used. Three
common measures, arithmetic mean, geometric mean and median values, were
calculated for each water body category and size class. To assess the most
suitable measure of centrality for water body emissions, normal probability
plots of raw and log-transformed emissions data were generated and tested
using the Shapiro–Wilks test (Fig. A5). The emissions data from all replicate
measurements fitted a log-normal (p=0.081) but not a normal distribution
(p=0.0000) and, therefore, the geometric mean would provide the most
appropriate measure of centrality for this data (Ott, 1994; Limpert et al.,
2001). Fluxes were scaled to annual rates using the cumulative surface area
of water bodies and the respective emissions rate for each size class using
the geometric means. The variability in geometric mean was given by the
exponential of the 95 % confidence interval range of log-transformed
data. Emissions for water bodies less than 3500 m2 were scaled using
stock dam rates and larger water bodies (3500 to 105 m2) using
rates obtained from irrigation dams and urban lakes. Total fluxes from
respective size classes were then combined to provide regional estimates.
Annual fluxes of CH4 were converted to CO2 equivalents
assuming a 100-year global warming potential of 34 (IPCC, 2013).

3.1 Relative surface area of ponds

The statewide land use assessment identified 13 046 ponds across
Queensland, occupying a total surface area of approximately 467 km2
(Fig. 2c). However, with the inclusion of the additional Reservoir and Water
Storage Point datasets the number of ponds increased over 20 times to a total
of 293 346, and the surface area more than doubled to 1087 km2. The
official land use assessment of Queensland underestimates the surface area of
ponds by 57 % and the total number of water bodies by more than 1 order
of magnitude. The revised total surface area of all artificial water bodies
across Queensland increased by 24 % to just over 3248 km2
(Table A2).

Ponds were widely distributed across the state, but over 78 % of ponds
were located on grazing land, suggesting that stock dams represent the
primary water body type (Fig. 2a). Over two-thirds of ponds were confined to
regions of the state where rainfall isohyets were above 600 mm (Fig. 2b) and
heavily concentrated in cropping and residential areas in the central and
south-eastern parts of the state (Fig. 2c). These findings highlight the
importance of striving to incorporate all artificial water bodies into
flooded land emission assessments; omitting water bodies below a size
threshold can lead to a dramatic underestimation of the total number of
water bodies present and a considerable underestimate of the available
surface area for CH4 emissions.

3.2 CH4 emissions from ponds

All 22 water bodies monitored in this study were shown to be emitters of
CH4, and emission rates ranged from a minimum of
1 mg m−2 d−1 to a maximum of 5425 mg m−2 d−1
(Table A1). Only one water body (Mt Larcom 3) had a maximum rate below the
reported upper range (50 mg CH4 m−2 d−1) for
diffusive fluxes found in larger water bodies in this region (Grinham et al.,
2011; Musenze et al., 2014). Mean flux rates of only four individual water
bodies were below 50 mg m−2 d−1 (Table A1), suggesting ebullition
to be the dominant emission pathway in these systems.

Grouping ponds according to their primary use resulted in no significant
differences in emissions rates between irrigation dams, stock dams and urban
lakes; however, weirs were significantly higher (F(3,121)=6.43,
p < 0.001) than all other categories (Fig. 3a). Mean emission
rates were, however, higher in stock water bodies (168 mg m−2 d−1)
compared with irrigation and urban bodies (84 and
129 mg m−2 d−1, respectively). Weir water bodies had mean
emission rates of 730 mg m−2 d−1, which is more than 4 times higher
than those of any other category (Fig. 3a). Grouping ponds according to their
GRanD size classes resulted in significantly higher emission rates
(KW H(2,121)=7.354, p < 0.05) from ponds in the 102 to
103 m2 size class compared to 104 to 105 m2
(Fig. 3b). Overall, mean emissions decreased with increasing size class. Note
that all weir sites fell into the smallest size category.

3.3 Spatial and temporal variability in surface area and emission rate

3.3.1 Spatial and temporal variability within a single pond

Observed emissions rates from the intensive spatial study, carried out in
December 2017, ranged over 2 orders of magnitude from under
40 to over 3500 mg m−2 d−1 (Fig. 4).
Emissions were highest in the shallow south-west sector of the pond, adjacent
a large storm water inflow point, as well as along the western boundary where
numerous overhanging riparian trees are located along with a second
storm water inflow point (Fig. 4).

Figure 6Variability in water surface area as a percentage of
AFSL between three GRanD database size classes of ponds. Values
indicate mean surface area ±SE (standard error) and 95 % CI
(confidence intervals).

3.3.2 Variability in water surface area across all monitored ponds

Variability in water surface area is strongly related to water body size
class (Fig. 6). Mean surface area within the smallest size class was only
64 % of AFSL; this increased to 84 % in the intermediate
size class and to 94 % in the largest size class (Fig. 6). Smaller ponds
had a significantly lower surface area relative to AFSL
(KW H(2,231)=50.523, p < 0.001) compared to larger size
classes and were more variable (Fig. 6). Regional emissions estimates
therefore need to correct for the differences in water body surface area
relative to predicted AFSL, particularly in the smaller size
classes.

3.4 Effect of inundation on stock dam emissions

The water surface area of a single stock dam ranged from 395 to 2808 m2
over a 40-month period (Fig. 7a) with an outer band of 580 m2
undergoing frequent inundation cycles (May 2016 to December 2017 – Fig. 7a).
Emissions rates from peripheral areas during an inundated period were
significantly higher (more than 1 order of magnitude) compared with
emissions when not inundated (KW H(1,10)=6.818, p < 0.001;
Fig. 7b). In contrast emissions from central areas were over
100 mg m−2 d−1, which is more than double the peripheral area emission
rates (Table A1). This modifier of rates will primarily impact emissions from
smaller size classes which have greater variability in water surface area
(Fig. 6). An additional implication is in the importance of designing
monitoring studies where emissions rates are quantified from both peripheral
and central areas for each system. Rates monitored only in peripheral areas
will likely bias towards lower emissions, particularly if these have
undergone recent inundation.

The findings of this study demonstrate ponds are an underreported and
important CH4 emission source in Queensland and likely also
globally. These findings highlight the importance of striving to incorporate
all artificial water bodies into flooded land emission assessments; omitting
water bodies below a size threshold can lead to a substantial
underestimation of the total number of water bodies present and a
considerable underestimate of the available surface area for CH4
emissions. Mean annual CH4 fluxes from ponds for the state of
Queensland ranged between 1.7 and
1.9 million t CO2 eq.
(Table 1) depending on the scaling approach. Given that ponds represent 33.5 %
of the total flooded lands surface area in Queensland and emission rates are
equivalent to larger water bodies in the region (Musenze et al., 2014; Sturm
et al., 2014), ponds represent one-third of total emissions from flooded
lands in Queensland. Remarkably, mean total emissions from ponds represent
approximately 10 % of Queensland's land use, land use change and forestry
sector (NGERS, 2015) emissions using either scaling approach.

Table 1Summary of Queensland small water bodies classified using two
different relative size classifications. The number of water bodies,
corrected surface area of size class and total mean annual emissions.
Approach 1: emissions for water bodies less than 3500 m2 were assumed
to be stock dams and larger water bodies were assumed to be irrigation dams (Fig. 3a).
Approach 2: emissions for GRanD size classes were taken from Fig. 3b.
However, weir emissions were omitted as these are not relevant at the
regional scale.

Future regional and global emissions estimates would be greatly improved with
the inclusion of ponds, as their proliferation has been noted in five
continents. In the continental United States ponds have been shown to cover
20 % of the total artificial water body surface area (Smith et al., 2002); in
South Africa there are an estimated 500 000 ponds (Mantel et al., 2010); in
Czechoslovakia ponds make up over 30 % of the total artificial water
body surface area (Vacek, 1983); and in India ponds are estimated to comprise
6238 km2, or over 25 % of India's artificial water body surface
area (Panneer Selvam et al., 2014).

4.2 Pond emission pathways

Emissions rates from ponds observed in this study are consistent with
ebullition being the dominant pathway. Diffusive emissions from studies of
three larger water bodies in the region found the upper limit for diffusive
emission was 50 mg m−2 d−1 (Grinham et al., 2011; Musenze et al.,
2014) and only five ponds had emission rates below this level. Ebullition was
observed at all ponds with maximum rates all in excess of
50 mg m−2 d−1,
with the exception of only one stock dam (Mt Larcom 3) where the
maximum rate was 19 mg m−2 d−1. This is a consistent finding
with larger water bodies in the region where ebullition has been shown to
dominate total emissions (Grinham et al., 2011; Sturm et al., 2014). The
relatively higher emissions from smaller pond size classes is consistent with
previous observations of increased ebullition activity in shallow zones,
particularly water depths less than 5 m (Keller and Stallard, 1994; Joyce and
Jewell, 2003; Sturm et al., 2014). Virtually all ponds within the smaller size
classes would be less than 5 m deep. In addition, ponds trap large
quantities of sediment and organic material (Neil and Mazari,
1993; Verstraeten and Prosser, 2008) and these deposition zones have been
identified as methane ebullition hotspots in larger water bodies (Sobek et
al., 2012; Maeck et al., 2013). The patterns in emissions from the intensive
spatial study in an urban lake, where shallow areas adjacent storm water
inflows were shown to be ebullition hotspots, have also been observed in
larger water bodies were ebullition activity was highest adjacent to
catchment inflows (DelSontro et al., 2011; Grinham et al., 2017; de Mello et
al., 2017). The emissions from small weirs were clearly dominated by
ebullition, which is consistent with emissions from three larger weirs where
rates ranged from 1000 to over 6000 mg m−2 d−1
(Bednařík et al., 2017). Weirs intercept the primary streamflow
pathways and will likely cause large quantities of catchment-derived organic
matter to deposit within the weir body which, coupled to the shallow nature,
results in high rates of ebullition. Overall, the rates observed for all
categories, except irrigation dams, were in the upper range of reservoir
areal flux rates reported in global reviews (St. Louis et al., 2000; Bastviken
et al., 2011; Deemer et al., 2016), reflecting the dominance of the ebullition
pathway in ponds. An additional consideration for future studies of
ebullition patterns in ponds stems from recent studies of reservoirs which
found significant changes in ebullition intensity and ebullition distribution
as water levels decrease (Beaulieu et al., 2018; Hilgert et al., 2019). Under
decreasing water levels, deeper zones of ponds may begin bubbling or increase
the intensity of bubbling; this could potentially offset the reduction in
surface available for emissions and total emissions would remain relatively
constant.

4.3 Challenges in scaling emissions

Efforts to develop flooded land emission inventories rely heavily on the
emission rate used to scale the surface area of water bodies within selected
categories. Given the high variability in emission rates within and between
individual ponds and relatively low replication, it is critical to select an
appropriate measure of centrality (arithmetic mean, geometric mean or median)
in order to scale regionally and globally (Downing, 2010). For rice paddies,
septic tanks, peatlands and natural waters (Aselmann and Crutzen, 1989; Dise
et al., 1993; Diaz-Valbuena et al., 2011; Bridgham et al., 2006), the geometric
mean has been applied. Likewise, in this study the log-normal distribution of
emissions data indicated the geometric mean as the most appropriate measure
and the total emission rates using this measure fell within the reported
range from larger artificial water bodies in the region (Grinham et al.,
2011; Sturm et al., 2014). However, the geometric means for all water body
categories and size classes were less than half of their respective
arithmetic mean values (Fig. A6). For irrigation, stock and urban water
bodies, geometric mean values were actually outside of 95 % confidence
interval limit for the arithmetic mean (Fig. A6a, b). Geometric mean and
median values were similar across all water body categories and size classes,
and these measures, therefore, represent conservative emissions rates from
ponds. This raises an important issue with scaling ebullition-dominated water
bodies as there is always going to be a high likelihood of detecting a small
number of very high rates which will invariably give rise to log-normal data
distributions. Future studies will focus on determining whether the
conservative estimates generated through the use of geometric means
approximate the true emissions from ponds.

Continued efforts to quantify regional pond abundance, particularly smaller
size classes, should be a research priority as this will greatly improve the
surface area estimate of flooded lands used for upscaling greenhouse gas
emissions as well as their role in the global carbon cycle. The increased
coverage, availability and resolution of satellite imagery as well as more
sophisticated methods to identify water bodies (Verpoorter et al., 2014) will
support these efforts. However, it is critical to continually update regional
assessments as the annual increase in farm ponds has been estimated to be as
high as 60 % in some parts of the globe (Downing and Duarte, 2009).
Regional assessments should also correct for differences in pond surface
area, particularly in the smaller size classes, as this study has
demonstrated actual surface area can be significantly smaller than the
surface area at full supply level (AFSL). An additional
consideration is to ensure pond emission studies from different regions
include all relevant ponds types. For example, the use of ponds to increase
groundwater recharge is widespread across Southeast Asia (Giordano, 2009) and
these would need to be included in regional inventories.

Increasing both the number and type of pond within each size class in
emissions monitoring studies should be a research priority. This will allow
increased confidence in the selection of an appropriate measure of
centrality as well as reducing uncertainty in the expected range of emission
rates within each pond category. When designing a monitoring study it is
important to ensure emissions rates are quantified from both peripheral and
central areas for each pond. This study demonstrated that measurements taken
only in peripheral areas will likely bias towards lower emissions
particularly in ponds that experience rapid changes in water level and,
therefore, inundation status of peripheral areas. However, this was limited
to a single stock dam and additional pond types and size classes must be
examined before more confident generalisations can be made.

The high spatial variability in emission rates within ponds noted from this
study highlights the importance of ensuring chambers cover the widest
possible spatial scale during a measurement campaign. This will increase the
likelihood of detecting ebullition zones which are likely the dominant
emission pathway. However, this finding was from a single urban lake and
additional long-term temporal studies along with high-resolution spatial
surveys of different pond types and size classes are required to identify the
drivers of pond emission pathways. Research into both pond surface area and
CH4 emission rates will allow greater understanding of their
importance to flooded land emission inventories at both regional and global
scales.

Table A1Selected characteristics from individual
ponds showing primary use of each system; surrounding land use type; location
of system latitude (Lat) and longitude (Long); average surface area (SA)
in m2; mean, median, minimum (Min) and maximum (Max) methane emission
rates (mg m−2 d−1); and number of chamber measurements on
individual systems (Cham). Primary uses included the following: irrigation
for cropping; stock watering for cattle and horses; urban uses included storm
water management and aesthetic purposes; weirs for water supply and
streamflow monitoring. a indicates water bodies where repeat
sampling was conducted; b indicates water bodies where
deployments of less than 24 h were conducted. Geom mean is the geometric
mean; Arithm mean is the arithmetic mean.

Table A2Surface area (SA) of Queensland artificial water bodies
within each GRanD database size class showing the official land use
assessment estimates (QLUMP, 2018) and the revised estimates for the
smallest three size classes found in this study.

Figure A2Pond size from the emission study relative to the histogram of
the regional pond distribution of stock dams, irrigation dams and urban lakes.
The surface area of pond used in the emission study (Table A1). Histogram of
regional distribution of ponds was developed from the QLUMP, Reservoir and Water
Storage Points databases and separated into pond type depending on
surrounding land use: “grazing native vegetation” for stock dams;
“production from irrigated agriculture and plantations” for irrigation
dams; “intensive uses” for urban lakes with “mining” and
“manufacturing” land use within “intensive uses” were removed to ensure
only urban areas were selected. To incorporate the distribution of ponds
within the Water Storage Points database, it was assumed this would match
the distribution from the 100 individual ponds examined in Sect. 2.2 to
determine their average surface area.

AG conceived, designed and conducted the study and co-wrote the manuscript;
CDE, CEL, DB and BS conceived, designed the study and contributed to the
manuscript; SA, ND and MD conducted the study and contributed to the
manuscript.

We are grateful to the reviewers for their helpful comments and suggestions.
We gratefully acknowledge the following for providing access to ponds: Ross
and Lorraine Prange, Geoff and Maureen Gale, Mark Bauer, Stuart Green and
Thomas Connolly. In addition, we are grateful for the background information
regarding the primary use of the ponds. We gratefully acknowledge
Markus Fluggen for laboratory and logistical support.

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Artificial water bodies are a major source of methane and an important contributor to flooded land greenhouse gas emissions. Past studies focussed on large water supply or hydropower reservoirs with small artificial water bodies (ponds) almost completely ignored. This regional study demonstrated ponds accounted for one-third of flooded land surface area and emitted over 1.6 million t CO2 eq. yr−1 (10 % of land use sector emissions). Ponds should be included in regional GHG inventories.

Artificial water bodies are a major source of methane and an important contributor to flooded...